Traditionally, counterfactual explanations have been employed to help understand the limitations of machine learning methods.
To this end, methods for counterfactual explanations try to find an example that is as close as possible to the data points in the training set of a model.
Counterfactual explanation methods can either be model-specific or model-agnostic.
Model-specific methods utilize the internal training characteristic (e.g., the hyperparameters) of the underlying machine learning model  [1]}, [2]}, [3]}. Model-agnostic methods separate the explanations from the machine learning model. They encompass functionality that apply to any arbitrary models[4]}, [5]}, [6]}, [7]}, generally by only accessing the prediction function of a fitted model  [8]}. Schölkopf et. al.  [9]} discuss causal representation learning as an avenue for counterfactual explanation.
These methods operate on supervised machine learning models and attempt to find examples close to one of the data points.
